Senior Staff Machine Learning Systems Engineer, Ads ML Platform
New
R
Reddit, Inc.Machine Learning
Location: Remote - United StatesFull-TimeSenior
Salary$292,500 — $409,500 USD
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Job Details
- Experience
- 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems; 4+ years building or operating production ML infrastructure
- Required Skills
- KafkaKubernetesMachine LearningAirflowSparkBigQueryDistributed Systems
Requirements
- 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.
- 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.
- Led broad, ambiguous, multi-team platform initiatives from strategy through adoption.
- Built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.
- Deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.
- Experience working with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar.
- Ability to balance urgent customer needs with durable long-term architecture and reusable platform patterns.
- Proven ability to influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.
Responsibilities
- Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows.
- Align Ads ML platform priorities with Reddit’s broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate.
- Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency.
- Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity.
- Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service.
- Extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience.
- Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution.
- Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders.
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